T-SNN：用单纯形复形建模 EEG 解码脑状态
T-SNN: Temporal Simplicial Neural Network for EEG Decoding
一篇做脑电情绪识别的论文，思路是不用普通图，而是把脑区当成动态单纯形复形，SEED-VII 七分类上跑赢了图方法和 Transformer。
arXiv 论文提出 T-SNN（Temporal Simplicial Neural Network），将 EEG 记录表示为不断演化的单纯形复形序列，通过单纯形卷积加循环更新，同时学习高阶脑区交互及其时间演变。在七分类 SEED-VII 情绪识别任务上，T-SNN 在 trial-wise 和跨被试两种评测中均优于卷积、循环、图方法和 Transformer。加入眼动特征后性能进一步提升，可用于多模态脑状态解码。
T-SNN: Temporal Simplicial Neural Network for EEG Decoding
Decoding brain states requires models that capture both the evolution of neural activity and interactions among groups of brain regions. Existing EEG methods often treat recordings as multivariate time series or represent functional connectivity with pairwise graphs, leaving dynamic higher-order interactions largely unmodeled. We introduce the Temporal Simplicial Neural Network (T-SNN), which represents EEG recordings as sequences of evolving simplicial complexes. By combining simplicial convolutions with recurrent updates, T-SNN jointly learns higher-order interactions and their temporal evolution. On the seven-class SEED-VII emotion recognition task, T-SNN outperforms convolutional, recurrent, graph-based, and Transformer methods in both trial-wise and cross-subject evaluations. Incorporating eye-movement features further improves performance, demonstrating the framework's potential for multimodal brain-state decoding.
